Abstract
To compare the performance of the Chronic Conditions Warehouse (CCW) and the 38-condition Elixhauser Comorbidity Index in predicting all-cause mortality among Medicare beneficiaries with dementia, we used a national sample of 1,566,359 community-dwelling Medicare beneficiaries (age ≥65) with dementia, identified in 2018 claims data. Using elastic net logistic regression, we applied 30 CCW conditions and 38 Elixhauser comorbidities from 2018 to predict mortality at 30, 60, 180 days, and 1 year through December 31, 2019. Mortality rates were 2.42% (30 days), 4.27% (60 days), 10.77% (180 days), and 19.0% (1 year). All models demonstrated good discrimination (C-statistics: 0.696-0.731) and calibration, with no meaningful performance differences between the two measures. Elastic net models produced parsimonious predictors with performance comparable to traditional logistic regression. Both CCW and Elixhauser measures predicted all-cause mortality in dementia with similar accuracy. Elastic net offers a robust approach to claims-based mortality prediction.